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Advanced Information Technology, Electronic and Automation Control
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Advanced Information Technology, Electronic and Automation Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10682

Special Issue Editors

Prof. Dr. Steven Guan

E-Mail Website
Guest Editor
Research Institute of Big Data Analytics, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: machine learning; computational intelligence; big data analytics; mobile commerce; modeling; networking; personalization; security; coding theory; pseudorandom number generation
Special Issues, Collections and Topics in MDPI journals
Dr. Yinong Chen

E-Mail Website
Guest Editor
Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-8809, USA
Interests: service-oriented computing; IoT; robotics; AI in education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue encourages authors from academia and industry to submit new research results in the fields of Information Technology, Communications, Network, Electronics, and Automation Control. It provides opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations, and to find global partners for future collaboration.

Topics that will be considered in this Special Issue include but are not limited to the following:

Topic 1: Artificial Intelligence, Automation and Control;

Topic 2: Machine Learning;

Topic 3: Communication and Networking;

Topic 4: Electronic and Embedded Systems;

Topic 5: Applied Mathematics, Computational Methods and Algorithms, Data and Signal Processing.

Prof. Dr. Steven Guan
Dr. Yinong Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor technology and application
  • artificial intelligence
  • machine learning
  • big data
  • computational methods and algorithms
  • internet of things
  • communications and signal processing

Published Papers (4 papers)

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Research

12 pages, 1570 KiB  
Article
Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction
by Zhengni Yang, Rui Yang and Mengjie Huang
Sensors 2021, 21(23), 7894; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237894 - 26 Nov 2021
Cited by 14 | Viewed by 1707
Abstract
Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due [...] Read more.
Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi’an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues. Full article
(This article belongs to the Special Issue Advanced Information Technology, Electronic and Automation Control)
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14 pages, 1743 KiB  
Article
Accent Recognition with Hybrid Phonetic Features
by Zhan Zhang, Yuehai Wang and Jianyi Yang
Sensors 2021, 21(18), 6258; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186258 - 18 Sep 2021
Cited by 10 | Viewed by 3139
Abstract
The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language [...] Read more.
The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method. Full article
(This article belongs to the Special Issue Advanced Information Technology, Electronic and Automation Control)
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20 pages, 2342 KiB  
Article
Effectiveness Evaluation Method of Application of Mobile Communication System Based on Factor Analysis
by Guohui Jia and Jie Zhou
Sensors 2021, 21(16), 5414; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165414 - 10 Aug 2021
Cited by 3 | Viewed by 1902
Abstract
The application mode of army mobile communication networks is closely related to combat mission and application environment. Different combat missions and application environments result in different network structures and different service priorities, which requires a semi-automatic system to support the network scheme design. [...] Read more.
The application mode of army mobile communication networks is closely related to combat mission and application environment. Different combat missions and application environments result in different network structures and different service priorities, which requires a semi-automatic system to support the network scheme design. Therefore, evaluating the efficiency of network schemes generated by automatic planning is a problem that needs to be urgently addressed. In the past, researchers have proposed a variety of methods to evaluate the effectiveness of mobile communication systems, most of which are based on simulation methods and ignore the historical data of network usage. This paper studies an effectiveness evaluation method of mobile communication network design schemes and proposes a design scheme for the evaluation and optimization of network plans. Furthermore, the improved method of effectiveness evaluation based on factor analysis is discussed in detail. The method not only effectively uses historical data but also reduces the amount of data collection and calculation. In order to adapt to the preference requirements of different task scenarios, a decision preference setting method based on cluster analysis is proposed, which can render the output optimization result more reasonable and feasible. Full article
(This article belongs to the Special Issue Advanced Information Technology, Electronic and Automation Control)
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14 pages, 545 KiB  
Communication
CMBF: Cross-Modal-Based Fusion Recommendation Algorithm
by Xi Chen, Yangsiyi Lu, Yuehai Wang and Jianyi Yang
Sensors 2021, 21(16), 5275; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165275 - 04 Aug 2021
Cited by 4 | Viewed by 3045
Abstract
A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, [...] Read more.
A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results. Full article
(This article belongs to the Special Issue Advanced Information Technology, Electronic and Automation Control)
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